Quality control is an important issue in semiconductor manufacturing. Statistical process control (SPC) is known as a powerful method for accomplishing process stability and reducing variability. In this paper, we adopt the quality-oriented statistical process control (QOSPC) method. In QOSPC, product quality test data, such as electrical performance and product reliability, are incorporated into the process control procedure. QOSPC has two major challenges: extracting process variables that affect product quality, and determining quality control limits (QCLs) for each variable. In this work, we fully exploit a Bayesian approach to resolve both of these challenges simultaneously. We introduced a linear bathtub model that contains parameters corresponding to QCLs as obvious change points and fit the model to the observed data by Bayesian inference (BI). In our experiments with artificial datasets, we demonstrated that the values of QCLs and their confidence, by which we can judge whether the measured process variable is related to product quality, are estimated successfully by BI. We verified the robustness of our method by testing it repeatedly. The proposed method reduced the human labor cost for extracting quality-related process variables and determining QCLs by 93%.

中文翻译：

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利用贝叶斯建模的面向质量的统计过程控制

质量控制是半导体制造中的一个重要问题。统计过程控制 (SPC) 被称为实现过程稳定性和减少可变性的强大方法。在本文中，我们采用面向质量的统计过程控制（QOSPC）方法。在 QOSPC 中，产品质量测试数据，如电气性能和产品可靠性，被纳入过程控制程序。QOSPC 面临两大挑战：提取影响产品质量的过程变量，以及确定每个变量的质量控制限 (QCL)。在这项工作中，我们充分利用贝叶斯方法来同时解决这两个挑战。我们引入了一个线性浴缸模型，其中包含与 QCL 对应的参数作为明显的变化点，并通过贝叶斯推理 (BI) 将模型拟合到观察到的数据。在我们对人工数据集的实验中，我们证明了通过 BI 成功估计了 QCL 的值及其置信度，通过它们我们可以判断测量的过程变量是否与产品质量相关。我们通过反复测试验证了我们方法的稳健性。所提出的方法将提取质量相关过程变量和确定 QCL 的人力成本降低了 93%。我们通过反复测试验证了我们方法的稳健性。所提出的方法将提取质量相关过程变量和确定 QCL 的人力成本降低了 93%。我们通过反复测试验证了我们方法的稳健性。所提出的方法将提取质量相关过程变量和确定 QCL 的人力成本降低了 93%。